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[TGV07] Kernel logistic PLS: A tool for supervised nonlinear dimensionality reduction and binary classification

Revue Internationale avec comité de lecture : Journal Computational Statistics & Data Analysis, vol. 51(9), pp. 4083-4100, 2007

Mots clés: PLS, KL-PLS

Résumé: “Kernel logistic PLS” (KL-PLS) is a new tool for supervised nonlinear dimensionality reduction and binary classification. The principles of KL-PLS are based on both PLS latent variables construction and learning with kernels. The KL-PLS algorithm can be seen as a supervised dimensionality reduction (complexity control step) followed by a classification based on logistic regression. The algorithm is applied to 11 benchmark data sets for binary classification and to three medical problems. In all cases, KL-PLS proved its competitiveness with other state-of-the-art classification methods such as support vector machines. Moreover, due to successions of regressions and logistic regressions carried out on only a small number of uncorrelated variables, KL-PLS allows handling high-dimensional data. The proposed approach is simple and easy to implement. It provides an efficient complexity control by dimensionality reduction and allows the visual inspection of data segmentation.

Equipe: msdma

BibTeX

@article {
TGV07,
title="{Kernel logistic PLS: A tool for supervised nonlinear dimensionality reduction and binary classification}",
author="A. Tenenhaus and A. Giron and E. Viennet and M. Bera and G. Saporta and B. Fertil",
journal="Computational Statistics & Data Analysis",
year=2007,
volume=51,
number=9,
pages="4083-4100",
}